Nanorobot queue: Cooperative treatment of cancer based on team member
communication and image processing
- URL: http://arxiv.org/abs/2111.11236v2
- Date: Tue, 23 Nov 2021 12:14:19 GMT
- Title: Nanorobot queue: Cooperative treatment of cancer based on team member
communication and image processing
- Authors: Xinyu Zhou
- Abstract summary: Most effective way to treat cancer diseases at this stage is through chemotherapy and radiotherapy.
This paper proposes an ideal model of a treatment method that can completely cure cancer, a cooperative treatment method based on nano robot queue through team member communication and computer vision image classification (target detection)
- Score: 6.1587841288357135
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Although nanorobots have been used as clinical prescriptions for work such as
gastroscopy, and even photoacoustic tomography technology has been proposed to
control nanorobots to deliver drugs at designated delivery points in real time,
and there are cases of eliminating "superbacteria" in blood through nanorobots,
most technologies are immature, either with low efficiency or low accuracy,
Either it can not be mass produced, so the most effective way to treat cancer
diseases at this stage is through chemotherapy and radiotherapy. Patients are
suffering and can not be cured. Therefore, this paper proposes an ideal model
of a treatment method that can completely cure cancer, a cooperative treatment
method based on nano robot queue through team member communication and computer
vision image classification (target detection).
Related papers
- AI Assisted Cervical Cancer Screening for Cytology Samples in Developing Countries [0.18472148461613155]
Cervical cancer remains a significant health challenge, with high incidence and mortality rates.
Conventional Liquid-Based Cytology(LBC) is a labor-intensive process, requires expert pathologists and is highly prone to errors.
This paper introduces an innovative approach that integrates low-cost biological microscopes with our simple and efficient AI algorithms for automated whole-slide analysis.
arXiv Detail & Related papers (2025-04-29T05:18:59Z) - Simulation of Nanorobots with Artificial Intelligence and Reinforcement Learning for Advanced Cancer Cell Detection and Tracking [0.0]
This study presents a new reinforcement learning framework for optimizing nanorobot navigation in complex biological environments.
We utilize a computer simulation model to explore the behavior of nanorobots in a three-dimensional space with cancer cells and biological barriers.
The proposed method uses Q-learning to refine movement strategies based on real-time biomarker concentration data, enabling nanorobots to autonomously navigate to cancerous tissues for targeted drug delivery.
arXiv Detail & Related papers (2024-11-04T18:16:40Z) - BreastRegNet: A Deep Learning Framework for Registration of Breast
Faxitron and Histopathology Images [0.05454343470301196]
This study introduces a deep learning-based image registration approach trained on mono-modal synthetic image pairs.
The models were trained using data from 50 women who received neoadjuvant chemotherapy and underwent surgery.
arXiv Detail & Related papers (2024-01-18T08:23:29Z) - Cancer-Net PCa-Gen: Synthesis of Realistic Prostate Diffusion Weighted
Imaging Data via Anatomic-Conditional Controlled Latent Diffusion [68.45407109385306]
In Canada, prostate cancer is the most common form of cancer in men and accounted for 20% of new cancer cases for this demographic in 2022.
There has been significant interest in the development of deep neural networks for prostate cancer diagnosis, prognosis, and treatment planning using diffusion weighted imaging (DWI) data.
In this study, we explore the efficacy of latent diffusion for generating realistic prostate DWI data through the introduction of an anatomic-conditional controlled latent diffusion strategy.
arXiv Detail & Related papers (2023-11-30T15:11:03Z) - Deep learning-based instance segmentation for the precise automated
quantification of digital breast cancer immunohistochemistry images [1.8434042562191815]
We demonstrate the feasibility of using a deep learning-based instance segmentation architecture for the automatic quantification of both nuclear and membrane biomarkers applied to IHC-stained slides.
We have collected annotations over samples of HE, ER and Ki-67 (nuclear biomarkers) and HER2 (membrane biomarker) IHC-stained images.
We have trained two models, so-called nuclei- and membrane-aware segmentation models, which, once successfully validated, have revealed to be a promising method to segment nuclei instances in IHC-stained images.
arXiv Detail & Related papers (2023-11-22T22:23:47Z) - Radiology Report Generation Using Transformers Conditioned with
Non-imaging Data [55.17268696112258]
This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information.
The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
arXiv Detail & Related papers (2023-11-18T14:52:26Z) - A Gamified Interaction with a Humanoid Robot to explain Therapeutic
Procedures in Pediatric Asthma [0.34410212782758043]
In chronic diseases, obtaining a correct diagnosis and providing the most appropriate treatments often is not enough to guarantee an improvement of the clinical condition of a patient.
This is generally true especially for certain diseases and specific target patients, such as children.
An engaging and entertaining technology can be exploited in support of clinical practices to achieve better health outcomes.
arXiv Detail & Related papers (2023-06-07T13:30:24Z) - Robotic Navigation Autonomy for Subretinal Injection via Intelligent
Real-Time Virtual iOCT Volume Slicing [88.99939660183881]
We propose a framework for autonomous robotic navigation for subretinal injection.
Our method consists of an instrument pose estimation method, an online registration between the robotic and the i OCT system, and trajectory planning tailored for navigation to an injection target.
Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method.
arXiv Detail & Related papers (2023-01-17T21:41:21Z) - Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology:
AI-Based Decision Support System for Gastric Cancer Treatment [50.89811515036067]
Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early stage, reducing GC-associated mortality rate.
We propose a practical AI system that enables five subclassifications of GC pathology, which can be directly matched to general GC treatment guidance.
arXiv Detail & Related papers (2022-02-17T08:33:52Z) - In-Line Image Transformations for Imbalanced, Multiclass Computer Vision
Classification of Lung Chest X-Rays [91.3755431537592]
This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data.
Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states.
This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
arXiv Detail & Related papers (2021-04-06T02:01:43Z) - Review of Artificial Intelligence Techniques in Imaging Data
Acquisition, Segmentation and Diagnosis for COVID-19 [71.41929762209328]
The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world.
Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19.
The recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists.
arXiv Detail & Related papers (2020-04-06T15:21:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.